html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/1887#issuecomment-744463486,https://api.github.com/repos/pydata/xarray/issues/1887,744463486,MDEyOklzc3VlQ29tbWVudDc0NDQ2MzQ4Ng==,43274047,2020-12-14T14:07:32Z,2020-12-14T15:47:18Z,NONE,"Just wanted to confirm, that boolean indexing is indeed highly relevant, especially for assigning values instead of just selecting them. **Here is a use case** which I encounter very often:

I'm working with very sparse data (e.g a satellite image of some islands surrounded by water), and I want to modify it using `some_vectorized_function()`. Of course I could use `some_vectorized_function()` to process the whole image, but boolean masking allows me to save a lot of computations.

Here is how I would achieve this in numpy:

```
import numpy as np
import some_vectorized_function

image = np.array(                                          # image.shape == (3, 7, 7)
    [[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 454, 454, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 565, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 343, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
    
     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 454, 565, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 667, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 878, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
    
     [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 565, 676, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 323, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 545, 0.0],
      [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]]
)
image = np.moveaxis(image, 0, -1)                          # image.shape == (7, 7, 3)


# ""image"" is a standard RGB image
# with shape == (height, width, channel)
# but only 4 pixels contain relevant data!


mask = np.all(image > 0, axis=-1)                          # mask.shape == (7, 7)
                                                           # mask.dtype == bool
                                                           # mask.sum() == 4

image[mask] = some_vectorized_function(image[mask])        # len(image[mask]) == 4
                                                           # image[mask].shape == (4, 3)
```

The most important fact here is that `image[mask]` is just a list of 4 pixels, which I can process and then **assign them back** into their original place. And as you see, this boolean masking also plays very nice with broadcasting, which allows me to mask a 3D array with a 2D mask.

Unfortunately, nothing like this is currently possible with XArray. If implemented, it would enable some crazy speedups for operations like spatial interpolation, where we don't want to interpolate the whole image, but only some pixels that we care about. ","{""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,294241734